U.S. patent application number 12/271609 was filed with the patent office on 2009-05-14 for seismic data processing.
This patent application is currently assigned to TERRASPARK GEOSCIENCES, L.P.. Invention is credited to William Stanley Hammon, III.
Application Number | 20090122061 12/271609 |
Document ID | / |
Family ID | 40623291 |
Filed Date | 2009-05-14 |
United States Patent
Application |
20090122061 |
Kind Code |
A1 |
Hammon, III; William
Stanley |
May 14, 2009 |
SEISMIC DATA PROCESSING
Abstract
A suite of processes and tools for preprocessing data prior to
seismic interpretation including: voxel connectivity mapping,
seismic response reduction, voxel suppression and voxel density.
Voxel connectivity is used to assist with removing insignificant
data. Seismic response reduction is used to reduce the seismic
response of a given reflector to a lobe, such as a main lobe. Voxel
suppression assists with highlighting and enhancing lithologic
boundaries to aid in human and computer-aided interpretation. Voxel
density scores the local significance of data trends within a
volume, such as a 3-D seismic volume, to assist with the
enhancement of these trends.
Inventors: |
Hammon, III; William Stanley;
(Boulder, CO) |
Correspondence
Address: |
SHERIDAN ROSS PC
1560 BROADWAY, SUITE 1200
DENVER
CO
80202
US
|
Assignee: |
TERRASPARK GEOSCIENCES,
L.P.
Westminster
CO
|
Family ID: |
40623291 |
Appl. No.: |
12/271609 |
Filed: |
November 14, 2008 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
60987906 |
Nov 14, 2007 |
|
|
|
Current U.S.
Class: |
345/424 |
Current CPC
Class: |
G06T 5/002 20130101;
G06T 5/008 20130101; G06T 2207/20032 20130101; G01V 1/345 20130101;
G06T 2207/20192 20130101; G01V 2210/644 20130101; G01V 1/302
20130101; G06T 2207/20012 20130101; G01V 2210/74 20130101; G06T
5/20 20130101; G01V 2210/641 20130101 |
Class at
Publication: |
345/424 |
International
Class: |
G06T 17/00 20060101
G06T017/00 |
Claims
1. A computer implemented method for processing seismic data
comprising: receiving a seismic data volume; determining, with the
assistance of a processor, a voxel connectivity score for
constituent members of one or more connected features in the data
volume; collapsing reflections within the data volume to reduce
seismic reflections; emphasizing high amplitude events using voxel
suppression; scoring the local significance of data trends in the
seismic data volume; and saving an output data volume.
2. The method of claim 1, wherein the output data volume is
utilized by a seismic interpretation system to do one or more of
interpret salt bodies, interpret canyons, interpret channels,
interpret horizons, surface wrapping and surface draping, which can
be output and visualized on a display.
3. The method of claim 2, wherein the visualization is a 3-D
rendering of geologic formations.
4. The method of claim 1, further comprising mapping connected
non-null voxels.
5. The method of claim 4, further comprising determining a
connectivity score based on a number of constituent voxels.
6. The method of claim 5, further comprising filtering one or more
features based on one or more of a score and a range of scores.
7. The method of claim 6, further comprising storing a
visual-clutter reduced seismic data volume.
8. The method of claim 1, further comprising determining if a
highest absolute amplitude voxel is at a center of an operator.
9. The method of claim 8, further comprising determining an extent
of a reflection lobe.
10. The method of claim 1, further comprising sorting a portion of
voxels by absolute value.
11. The method of claim 10, further comprising preserving a
percentage of the portion of voxels above a certain value or
absolute value.
12. The method of claim 11, further comprising resealing to
emphasize voxels at a center of an operator.
13. The method of claim 1, further comprising counting a number of
voxels within a given threshold range.
14. The method of claim 13, further comprising determining and
outputting a density score of a window's center voxel.
15. The method of claim 14, further comprising saving and
outputting a density score volume including a volumetric confidence
estimate.
16. A computer implemented method for processing seismic data in a
data volume comprising: receiving the seismic data volume; and
determining, with the assistance of a processor, a voxel
connectivity score for constituent members of one or more connected
features in the data volume.
17. The method of claim 16, further comprising mapping connected
non-null voxels.
18. The method of claim 17, further comprising determining a
connectivity score based on a number of constituent voxels.
19. The method of claim 18, further comprising filtering one or
more features based on one or more of a score and a range of
scores.
20. The method of claim 19, further comprising storing a
visual-clutter reduced seismic data volume.
21. A computer implemented method of processing a seismic data
volume comprising: receiving the seismic data volume; and
collapsing reflections within the data volume to reduce seismic
reflections.
22. The method of claim 21, further comprising determining if a
highest absolute amplitude voxel is at a center of an operator.
23. The method of claim 22, further comprising determining an
extent of a reflection lobe.
24. A computer implemented method for processing seismic data
comprising: receiving the seismic data volume; emphasizing high
amplitude events using voxel suppression; and sorting a portion of
voxels by absolute value.
25. The method of claim 24, further comprising preserving a
percentage of the portion of voxels above a certain value.
26. The method of claim 25, further comprising resealing to
emphasize voxels at a center of an operator.
27. A computer implemented method for processing seismic data
comprising: receiving a seismic data volume; scoring the local
significance of data trends in the seismic data volume; counting a
number of voxels within a given threshold range; and determining
and outputting a density score of a window's center voxel.
28. The method of claim 27, further comprising saving and
outputting a density score volume including a volumetric confidence
estimate.
29. A system for processing seismic data comprising: an I/O
interface adapted to receive a seismic data volume; a voxel
connectivity module adapted to determine, with the assistance of a
processor, a voxel connectivity score for constituent members of
one or more connected features in the data volume; a reflection
collapse module adapted to collapse reflections within the data
volume to reduce seismic reflections; a voxel suppression module
adapted to emphasize high amplitude events using voxel suppression;
and a voxel density module adapted to score the local significance
of data trends in the seismic data volume.
30. The system of claim 29, wherein the output data volume is
utilized by a seismic interpretation system to do one or more of
interpret salt bodies, interpret canyons, interpret channels,
interpret horizons and surface wrapping, surface draping, which can
be output and visualized on a display.
31. The system of claim 30, wherein the visualization is a 3-D
rendering of geologic formations.
32. The system of claim 29, wherein the voxel connectivity module
further maps connected non-null voxels.
33. The system of claim 32, wherein the voxel connectivity module
further determines a connectivity score based on a number of
constituent voxels.
34. The system of claim 33, wherein the voxel connectivity module
further filters one or more features based on one or more of a
score and a range of scores.
35. The system of claim 34, wherein the voxel connectivity module
in cooperation with a controller stores a visual-clutter reduced
seismic data volume.
36. The system of claim 29, wherein the reflection collapse module
further determines if a highest absolute amplitude voxel is at a
center of an operator.
37. The system of claim 36, wherein the reflection collapse module
further determines an extent of a reflection lobe.
38. The system of claim 29, wherein the voxel suppression module
further sorts a portion of voxels by absolute value.
39. The system of claim 38, wherein the voxel suppression module
further preserves a percentage of the portion of voxels above a
certain value or absolute value.
40. The system of claim 39, wherein the voxel suppression module
further rescales to emphasize voxels at a center of an
operator.
41. The system of claim 29, wherein the voxel density module
further counts a number of voxels within a given threshold
range.
42. The system of claim 41, wherein the voxel density module
further determines and outputs a density score at a window's center
voxel.
43. The system of claim 42, wherein an output a density score
volume is stored that includes a volumetric confidence
estimate.
44. A system for processing seismic data comprising: an I/O
interface adapted to receive the seismic data volume; and a voxel
connectivity module adapted to determine, with the assistance of a
processor, a voxel connectivity score for constituent members of
one or more connected features in the data volume.
45. The system of claim 44, wherein connected non-null voxels are
mapped.
46. The system of claim 45, wherein a connectivity score is
determined based on a number of constituent voxels.
47. The system of claim 46, further comprising a filtering module
adapted to filter one or more features based on one or more of a
score and a range of scores.
48. The system of claim 47, wherein a visual-clutter reduced
seismic data volume is stored.
49. A system for processing seismic data comprising: an I/O
interface adapted to receive the seismic data volume; and a
reflection collapser module adapted to collapse reflections within
the data volume to reduce seismic reflections.
50. The system of claim 49, wherein the reflection collapse module
further determines if a highest absolute amplitude voxel is at a
center of an operator.
51. The system of claim 50, wherein the reflection collapse module
further determines an extent of a reflection lobe.
52. A system for processing seismic data comprising: an I/O
interface adapted to receive the seismic data volume; a voxel
suppression module adapted to: emphasize high amplitude events
using voxel suppression; and sort a portion of voxels by absolute
value.
53. The system of claim 52, wherein the voxel suppression module
further preserves a percentage of the portion of voxels above a
certain value or absolute value.
54. The system of claim 53, wherein the voxel suppression module
further rescales to emphasize voxels at a center of an
operator.
55. A system for processing seismic data comprising: an I/O
interface adapted to receive a seismic data volume; a voxel density
module adapted to: score the local significance of data trends in
the seismic data volume; count a number of voxels within a given
threshold range; and determine and output a density score of a
window's center voxel.
56. The system of claim 55, wherein a density score volume
including a volumetric confidence estimate is saved.
Description
RELATED APPLICATION DATA
[0001] This application claims the benefit of and priority under 35
U.S.C. .sctn.119(e) to U.S. Patent Application No. 60/987,906,
filed 14 Nov. 2007, entitled "Seismic Data Processing," and is
related to PCT Application PCT/US2007/071733 (Published as
WO2008/005690), both of which are incorporated herein by reference
in their entirety.
BACKGROUND
[0002] In the related application mentioned above, processes are
described that assist with the identification of potential
hydrocarbon deposits that include performing a structural
interpretation of a three-dimensional seismic volume, transforming
the three-dimensional seismic volume into a stratal-slice volume,
performing a stratigraphic interpretation of the stratal-slice
volume which includes the extracting of bounding surfaces and
faults and transforming the stratal-slice volume into the spatial
domain. As illustrated, an exemplary seismic volume before domain
transformation is presented in FIG. 24a of the related application,
interpreted horizons and faults used in the transformation are
presented in FIG. 24b of the related application and the domain
transformed stratal-slice volume is presented in FIG. 24c of the
related application. The input seismic volume in FIG. 24a of the
related application has deformations associated with syn- and
post-depositional faulting. The output domain transformed volume
(FIG. 24c of the related application) is substantially free of
deformations.
[0003] This workflow and automated or semi-automated method and
system for identifying and interpreting depositional environments,
depositional systems and elements of depositional systems from 3-D
seismic volumes benefits from data pre-processing.
SUMMARY
[0004] It is an aspect of the present invention to provide systems,
methods and techniques for data processing.
[0005] It is another aspect of this invention to provide systems,
methods and techniques for seismic data pre-processing.
[0006] It is a further aspect of this invention to provide systems,
methods and techniques for 3-D seismic data pre-processing.
[0007] A further aspect of this invention is directed toward
determining a voxel connectivity score.
[0008] A still further aspect of this invention relates to reducing
data "clutter" based on the voxel connectivity score.
[0009] Still other exemplary aspects of the invention relate to
reducing a seismic response of a reflector to a lobe.
[0010] Still other exemplary aspects of the invention relate to
reducing a seismic response of a reflector to a main lobe.
[0011] Another exemplary aspect of the invention is directed toward
removing extraneous reflections in seismic data.
[0012] Further exemplary aspects of the invention relate to
highlighting and enhancing lithologic boundaries to assist with
interpretation of seismic data.
[0013] Additional aspects of the invention relate to scoring and
utilizing confidence in a 3-D data volume.
[0014] Still further aspects of the invention relate to using local
data redundancy to generate and output a stable estimate of
confidence in a data set.
[0015] Additional aspects of the invention relate to one or more
means for performing the steps enumerated in claims 1-28.
[0016] Additional aspects of the invention relate to a data volume
stored on a computer-readable storage media including data
representing geologic information formed in accordance with any one
or more of the steps in claims 1-28.
[0017] Additional aspects of the invention relate to a
computer-readable storage media having stored thereon instructions
that when executed by a processor performs the steps of any one of
claims 1-28.
[0018] These and other features and advantages of this invention
are described in, or are apparent from, the following detailed
description of the exemplary embodiments.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The exemplary embodiments of the invention will be described
in detail, with reference to the following figures. It should be
understood that the drawings are not necessarily shown to scale. In
certain instances, details which are not necessary for an
understanding of the invention or which render other details
difficult to perceive may have been omitted. It should be
understood, of course, that the invention is not necessarily
limited to the particular embodiments illustrated herein.
[0020] FIG. 1 illustrates an exemplary data processing system and
seismic interpretation system according to this invention;
[0021] FIG. 2 illustrates an exemplary method for determining voxel
connectivity according to this invention;
[0022] FIG. 3 illustrates an exemplary method for reducing
reflections according to this invention;
[0023] FIG. 4 illustrates an exemplary method of highlighting high
amplitude events in a data volume, such as a seismic volume;
[0024] FIG. 5 illustrates an exemplary voxel density estimation
process;
[0025] FIGS. 6(a-f) illustrate an exemplary application of voxel
connectivity with progressively higher connectivity score
thresholds to seismic data: a--input "sparse" seismic section;
b-f--input seismic data filtered by voxel connectivity with
connectivity scores progressively increasing from 100 in b to
20,000 in f;
[0026] FIGS. 7(a-c) illustrate an exemplary application of voxel
connectivity with progressively higher connectivity score
thresholds to seismic data: a--input "sparse" seismic section;
b-c--input seismic data filtered by Voxel Connectivity with
progressively increasing connectivity scores;
[0027] FIGS. 8(a-c) illustrate an exemplary application of voxel
connectivity with progressively higher connectivity score
thresholds to a different vertical seismic section from the same
seismic data volume used in FIG. 2: a--input "sparse" seismic
section; b-c--input seismic data filtered by Voxel Connectivity
with progressively increasing connectivity scores;
[0028] FIG. 9 illustrates an exemplary a "zero phase" wavelet
centered on a reflector--note the train of low amplitude side-lobes
above and below the main lobe;
[0029] FIGS. 10(a-d) illustrate an exemplary seismic section
processed using reflection collapser: a--input data; b--reflection
collapser processed section; c--reflection collapser processed
section for peaks only; d--reflection collapser processed section
for troughs only;
[0030] FIGS. 11(a-d) illustrate an exemplary reflection collapser
applied to a sparse input data set: a--input data; b--reflection
collapser processed section; c--reflection collapser processed
section for peaks only; d--reflection collapser processed section
for troughs only;
[0031] FIG. 12 illustrates an example of cosine taper scaling
factors for a two dimensional rectangular operator of dimensions
5.times.9;
[0032] FIGS. 13(a-b) illustrate an example of voxel suppression
applied to an example seismic volume: a--a section from the input
seismic volume; b--the same section from the volume filtered with
Voxel Suppression;
[0033] FIGS. 14(a-b) illustrate an example of voxel suppression
applied to the same example seismic volume as in FIG. 13: a--a
section from the input seismic volume; b--the same section from the
volume filtered with voxel suppression, this section is orthogonal
to the section displayed in FIG. 13;
[0034] FIGS. 15(a-b) illustrate an example of voxel suppression
applied to an second example seismic volume: a--a section from the
input seismic volume; b--the same section from the volume filtered
with voxel suppression;
[0035] FIGS. 16(a-c) illustrate an example of the numerical results
of voxel density calculations on a 10.times.10 data array: a--the
input two-dimensional array of data; b--the output two dimensional
array of data processed using a 3.times.3 voxel density operator
accepting all input values greater than or equal to 6; c--the
results of further constraining the output density score to be
greater than or equal to 4;
[0036] FIGS. 17(a-b) illustrate an example of the numerical results
of voxel density calculations on a 10.times.10 data array from FIG.
16a: a--the output two dimensional array of data processed as in
FIG. 16b with the additional constraint that the center voxel fall
in the specified threshold range; b--the results of applying a
minimum threshold of 4 to the density scores in FIG. 17a;
[0037] FIGS. 18(a-d) illustrate an example of a graphical
representation of the results described in FIGS. 16 and 17: a--the
same raw data as FIG. 16a; b--the results shown in FIG. 16c; c--the
results shown in FIG. 17b; d--the result of a simple thresholding
operation applied to the input data where no density calculation
was performed;
[0038] FIGS. 19(a-f) illustrate en exemplary comparison of some
standard data smoothing operators with density guided smoothing: a,
b--the results of applying a 3.times.3 mean and median filters,
respectively, to the raw data in FIG. 18a; c--the result of
applying the mean smoothing only to voxels that fail the minimum
density test; d--the result of applying a median operator only to
voxels that fail the minimum density test; e, f--the result of
modifying the selective smoothing to only include voxels that fall
outside the initial specified threshold range;
[0039] FIGS. 20(a-d) illustrate various exemplary described filters
applied to a horizontal slice through a continuity or coherence
volume showing a canyon: a--input data; b--removing voxels that
have a density score lower than a specified cutoff; c--the results
of applying confidence-adaptive smoothing where voxels that failed
the minimum density test and were outside the valid threshold range
were included in smoothing; d--the result of applying contrast
enhancement the data in a;
[0040] FIG. 21 illustrates various exemplary described filters
applied to a flat slice through a stratal volume showing a fluvial
channel: a--input data; b--the curvature response of the data in a;
c--the result of applying density threshold filtering to the
curvature data in b; d--the result of applying contrast enhancement
to the curvature data in b;
[0041] FIGS. 22(a-b) illustrate exemplary numerical and graphical
results of applying contrast enhancement to the sample data array
from FIG. 16a: a--numerical output array from contrast enhancement
with the histogram of the input raw data from FIG. 16a;
b--graphical output array from contrast enhancement with the
histogram of the output contrast enhanced data;
[0042] FIGS. 23(a-e) illustrate an exemplary effect of locally
adaptive voxel density-controlled smoothing and contrast
enhancement on a time slice from a Gulf of Mexico data set: a--the
raw seismic data that with a 3.times.3 median filter applied to
reduce random noise; b--the result of calculating coherence on the
data in a; c--the result of calculating the variance of the data in
b; d,e--the result of applying locally adaptive contrast
enhancement (d) and smoothing (e), controlled by the variance
distribution in c, to the data in b;
[0043] FIGS. 24(a-d) illustrate an exemplary effect of locally
adaptive voxel density-controlled smoothing and contrast
enhancement on a deeper time slice from the Gulf of Mexico data set
used in FIG. 23: a--the raw seismic data that with a 3.times.3
median filter applied to reduce random noise; b--the result of
calculating coherence on the data in a; c,d--the result of applying
locally adaptive contrast enhancement (c) and smoothing (d), to the
data in b;
[0044] FIGS. 25(a-f) illustrate exemplary effects of applying
contrast enhancement to coherence data on the output of a fault
enhance calculation: a--a coherence time slice showing a portion of
a salt body, with surrounding faults; b,c--the result of applying
two levels of contrast enhancement to the data in a; d--the fault
enhanced output using the raw coherence data (a) as input; e--the
fault enhanced output using the contrast enhanced data from (b) as
input; f--the fault enhanced output using the contrast enhanced
data (c) as input;
[0045] FIGS. 26(a-d) illustrate an example of voxel density applied
to coherence. Panel (a) contains a coherence image of a submarine
canyon. Panel (b) shows the result of applying binary voxel density
filtering to the data in Panel (a). Voxels that fail the minimum
density threshold test are assigned a null value. Panel (c) show
the result of voxel density controlled smoothing. voxel density
score are used to alter the data contrast in Panel (d). Voxel
Density-controlled smoothing and contrast enhancement preserve the
original context of the data, rather than simply removing voxels
that fail the density threshold test;
[0046] FIGS. 27(a-b) illustrate an example of a voxel suppression
result. Panel (a) contains a raw amplitude section. The top and
bottom of the horizontal tabular salt body are indicated by the
arrows in panel (a). Panel (b) shows the result of applying voxel
suppression to the data in panel (a);
[0047] FIGS. 28(a-d) illustrate an example of the reflection
collapser applied to sparse seismic data;
[0048] FIGS. 29(a-c) illustrate exemplary results of applying voxel
connectivity; and
[0049] FIGS. 30(a-d) illustrate an example of the workflow applied
to real data.
DETAILED DESCRIPTION
[0050] The exemplary embodiments of this invention will be
described in relation to processing and interpretation of data, and
in particular seismic data. However, it should be appreciated, that
in general, the systems and methods of this invention will work
equally well for any type of data representing any environment,
object or article.
[0051] The exemplary systems and methods of this invention will
also be described in relation to seismic data interpretation and
manipulation. However, to avoid unnecessarily obscuring the present
invention, the following description omits well-known structures
and devices that may be shown in block diagram form or otherwise
summarized.
[0052] For purposes of explanation, numerous details are set forth
in order to provide a thorough understanding of the present
invention. However, it should be appreciated that the present
invention may be practiced in a variety of ways beyond the specific
details set forth herein.
[0053] Furthermore, while the exemplary embodiments illustrated
herein show the various components of the system collocated, it is
to be appreciated that the various components of the system can be
located at distant portions of a distributed network, such as a
communications network and/or the Internet, or within a dedicated
secure, unsecured and/or encrypted system. Thus, it should be
appreciated that the components of the system can be combined into
one or more devices or collocated on a particular node of a
distributed network, such as a communications network. As will be
appreciated from the following description, and for reasons of
computational efficiency, the components of the system can be
arranged at any location within a distributed network without
affecting the operation of the system.
[0054] Furthermore, it should be appreciated that various links can
be used to connect the elements and can be wired or wireless links,
or any combination thereof, or any other known or later developed
element(s) that is capable of supplying and/or communicating data
to and from the connected elements. The term module as used herein
can refer to any known or later developed hardware, software,
firmware, or combination thereof that is capable of performing the
functionality associated with that element. The terms determine,
calculate and compute, and variations thereof, as used herein are
used interchangeably and include any type of methodology, process,
mathematical operation or technique, including those performed by a
system, such as a processor, an expert system or neural
network.
[0055] Additionally, all references identified herein are
incorporated herein by reference in their entirely.
[0056] FIG. 1 illustrates an exemplary data processing system 100
connected via a link to a seismic interpretation system 200. The
seismic interpretation system 200 can assist with the
interpretation of one or more of salt bodies, canyons, channels,
horizons, surface draping, or the like, as described in the
above-identified related application. The data processing system
100 comprises a voxel connectivity module 110, a reflection
collapse module 120, a controller 130, storage 140, one or more
computer-readable storage media 150 (on which software embodying
the techniques disclosed herein can be stored and executed with the
cooperation of the controller, memory, I/O interface and storage),
voxel suppression module 160, voxel density module 170, memory 180
and I/O interface 190, all connected by link(s) (not shown). The
system can further be associated with an output device, such as
computer display 300, on which the outputs of the various
techniques can be shown to a user.
[0057] The voxel connectivity module 110 assists with the mapping
of connected voxels. Seismic data volumes can be rendered sparse by
data processing steps designed to remove insignificant data points.
Similarly, some seismic attributes result in large areas of null or
undefined data. In both these cases, the null and undefined areas
are commonly speckled with insignificant data that `leak` through
the processing step used to create the volume. This visual clutter
can complicate the use of such volumes for segmentation or user or
computer-interpretation of important features present in the
volume. The removal of some or all of this visual clutter is one
exemplary goal for increasing the utility of these data
volumes.
[0058] An exemplary embodiment of the operation of the voxel
connectivity mapping module 110 determines which voxels are
constituent members of connected features in the data volume. The
`connectivity score` (how many voxels make up the feature) can then
be used to remove what are identified as small, and thus
insignificant, features through instituting a minimum feature size
threshold for the output volume.
[0059] An exemplary embodiment of voxel connectivity maps out all
connected non-null voxels in a volume. After mapping connected
voxels, the connectivity score of each connected feature in the
volume is defined as its number of constituent voxels. Visual
clutter can then be filtered by removing features from the output
data volume that have a connectivity score lower than some minimum
threshold. In this manner, small features are removed from the data
volume which can then be output and saved.
[0060] If voxel connectivity is applied to a sparse amplitude
volume, amplitude polarity can be used as an additional optional
constraint for connectivity mapping. For example, if mapping out a
positive amplitude reflection, only positive amplitudes are
considered to be non-null.
[0061] FIG. 6 demonstrates the application of progressively higher
connectivity score thresholds to the same data set. FIG. 6a
contains the raw seismic data rendered sparse by a separate process
(e.g., Voxel Suppression). A salt diapir exists in the center of
the data set. The significant amplitude events have been maintained
while removing low amplitude reflections. However, a considerable
amount of scattered, disconnected data points remain in the data
set. FIG. 6b shows the results of removing features that consist of
fewer than 100 connected voxels. This result represents a
significant improvement in reducing the amount of visual clutter.
FIGS. 6c, 6d, 6e, and 6f filter out features using a progressively
higher connectivity score threshold. FIG. 6f is overly aggressive
(using a connectivity score threshold of 20000) and has removed
portions of the salt boundary reflection.
[0062] FIGS. 7 and 8 contain different cross-section views from the
same data set. In each Figure, panel (a) shows raw data made sparse
by the same pre-process described above. Panels (b) & (c) show
a minimally filtered version of the data, and a more aggressively
filtered result, respectively. In FIGS. 7b and 7c, significant
reflections have been preserved while visual clutter has been
reduced. The exemplary results were generated using a connectivity
score threshold of 200. Similarly, FIG. 7c maintains significant
reflections while more aggressively filtering with a connectivity
score threshold of 800. However, the same threshold has filtered
too much in FIG. 8c. The more discontinuous, but still significant,
reflections at the base of the data section have been removed.
thus, care should be taken in selecting the connectivity score
threshold used for filtering.
[0063] The output volume from the voxel connectivity module 110
allows the user to remove unwanted visual clutter from a sparse
data set. Connected bodies are scored based on the number of
constituent voxels. Features with a connectivity score lower than a
user-specified threshold are then removed from the output data
volume. This technique can be a powerful tool for preparing data
sets for seismic interpretation such as segmentation.
[0064] Seismic reflections consist of a main amplitude response,
and several more minor flanking amplitude responses. These
additional responses, other than the main response, complicate the
use of amplitude volumes for computer segmentation of significant
reflectors. In most processed seismic data volumes, a zero phase
wavelet as illustrated in FIG. 9 is used. This means that the
actual reflector location is indicated by the location of the
maximum value of the main reflection response (or lobe). A zero
phase wavelet is also symmetric about the maximum reflection lobe.
There exist other extraneous lobes both above and below the main
lobe of interest. Removing these extra reflection lobes can result
in a much cleaner volume for both human and computer interpretation
of high amplitude events.
[0065] An exemplary embodiment of the operation of the reflection
collapser module 120 reduces the seismic response of a given
reflector to a main lobe. This removes `clutter` that may be
unnecessary for the interpretation of high amplitude reflections in
the seismic volume. Computer interpretation processes and
algorithms are also aided by removing extraneous reflections from a
seismic data volume.
[0066] In its most basic exemplary form, the reflection collapser
module 120, for example in cooperation with the controller 130,
convolves a 1-D operator with the input volume. For each operator
position, a test is run to see if the operator's center voxel has
the highest absolute amplitude of all the voxels contained within
the operator. If the highest absolute amplitude voxel is not in the
center of the operator, nothing is done and the operator moves to
the next voxel. However, if the highest absolute amplitude is at
the center of the operator, the module writes that voxel value to
the output volume in its original position. Two searches are then
performed to determine the extent of this reflection lobe.
[0067] The first search is performed upward from the center voxel.
The search extends upward until a zero-crossing is encountered. The
search then ceases. The second search is performed in a similar
manner in the downward direction. In this manner, the full extent
of the main reflection lobe is written to the output volume by the
module.
[0068] Other steps are also performed in order to ensure stability
of the module's performance. Local variations in amplitude, as well
as random noise, can cause a reflection side lobe to locally have a
greater absolute amplitude than the main reflection lobe. In order
to prevent this from introducing discontinuities to the main
reflection lobe, a pre-processing step can be performed to
regularize the amplitude of all reflection lobes present.
[0069] In this exemplary pre-processing step, all member voxels of
each reflection lobe are mapped using connected polarity analysis.
Connected polarity analysis is similar to connected threshold
analysis in that it determines which voxels are connected in a 3-D
body. The difference lies in the fact that the polarity of the
voxel is the only parameter used to determine connectivity, rather
than the use of a threshold range. Once all member voxels of a
reflection lobe are mapped, the mean amplitude of that lobe is
calculated. This mean is the amplitude value used to determine
which lobe in a reflection is the main reflection lobe. The main
process described above is then used to remove the side lobes of
the reflection.
[0070] FIGS. 10 and 11 illustrate the application of reflection
collapser module on an input seismic data volume. These views are a
vertical cross-section through the data set. There is a horizontal
band of salt in the middle of the data section.
[0071] FIG. 10a contains the raw data for this section. Both high
amplitude and low amplitude reflections are present. FIG. 10b shows
the result after applying the reflection collapser module process.
The side lobes of reflection have been removed from the data.
However, some local variations in reflection amplitude have caused
inconsistent behavior for which a reflection lobe is preserved as
the main lobe. FIGS. 10c and 10d, respectively, illustrate the
result of only considering positive or negative amplitudes in the
reflection collapser process. This could be desirable, for example,
when trying to enhance an interface with a known reflection
coefficient polarity (such as a sediment/salt interface).
[0072] FIG. 11a shows a sparse version of the data in FIG. 10a. The
data was made sparse by a separate process designed to remove low
amplitude reflections from the data set. FIG. 11b shows the
performance of the reflection collapser technique when considering
all reflections in this sparse data set. FIGS. 11c and 11d
illustrate the results of selectively considering only positive or
negative amplitudes, respectively. Reflection side lobes are
successfully removed in each data example, but the result can be
more stable when only one polarity is considered.
[0073] The exemplary reflection collapsor module 120 removes one or
more side lobes (either above and/or below a main lobe) from
reflectors in a seismic data volume. This removes unnecessary
clutter from a volume being used to interpret high amplitude
reflections. Human interpretation and computer segmentation of
these high-amplitude reflections can benefit from these data
processing techniques.
[0074] The expression of lithologic boundaries in a seismic data
volume can be quite complicated. In the case of salt or diagenetic
boundaries, they can cut through the data set in any imaginable
orientation and configuration. The manual interpretation of such
interfaces can be extremely time consuming when performed by hand.
The automation of this type of interpretation is a very important
research goal in seismic data interpretation. Voxel suppression is
a first step towards highlighting and enhancing the lithologic
boundaries to aid their human and computer-automated
interpretation.
[0075] An exemplary embodiment of voxel suppression is a method to
emphasize high amplitude events in a seismic volume. This is
accomplished by the voxel suppression module 160 rendering a volume
sparse, while maintain locally high amplitude events in their
original positions. Preserved voxel values can optionally be
resealed in order to boost the strength of weak events. This
resealing normalizes the expression of significant reflections
throughout the volume.
[0076] The exemplary voxel suppression module 160 convolves a
3-dimensional operator with the input seismic data volume. For each
operator position, all voxels within the operator are sorted by
absolute value. A user-specified (entered via a user input device
(not shown)) percent of the highest values are preserved in their
original position. This percent of preservation is typically small;
less than 15% for all applications.
[0077] If the user prefers, these preserved values can be resealed
by the voxel suppression module 160 to regularize the expression of
locally significant reflections throughout the volume. This is
accomplished by calculating the standard deviation of all the
voxels contained within the operator, and resealing these values to
make the local standard deviation match the standard deviation of
the whole volume. To accomplish this, the all preserved voxels are
multiplied by a resealing factor (RF). The RF is calculated as:
RF=Whole volume Std. Dev./Operator Std. Dev.
In some cases voxel values are boosted, while in other cases they
may be lowered. The end result is that all features preserved
within the volume have a similar appearance.
[0078] The data can also be resealed by the voxel suppression
module 160 to give emphasis to voxels in the center of the
operator. A radial cosine taper can be used to give more emphasis
to preserve voxels in the center of the operator, rather than at
its edges. This cosine taper rescales voxels based on their
distance from the center of the operator. The center voxel is
resealed by a factor of 1 (no change). The most distal voxels are
resealed by a factor of zero (zeroed out). In between, a sinusoidal
taper can define the rescale factor for each individual voxel
contained within the operator. FIG. 12 is a 2-dimensional example
of the cosine taper resealing factors for a rectangular operator of
dimensions 5.times.9.
[0079] This exemplary combination of steps can enhance locally
high-amplitude reflections, while removing extraneous surrounding
voxels. The result is a visually cleaner volume that is more easily
enhanced by other attributes for the purpose of automating
interpretation.
[0080] FIGS. 13, 14 and 15 show the results of applying voxel
suppression to real data volumes. These views are vertical
cross-sections through their respective data sets. FIGS. 13 and 14
are different cross-sections from the same data volume. They each
have a horizontal band of salt in the middle of the data section.
FIG. 15 is from a separate survey and has a salt diapir in the
center of the view.
[0081] FIG. 13a shows the raw data volume before running the voxel
suppression module 160 applies voxel suppression. Locally
high-amplitude reflections are preserved by the voxel suppression
technique (FIG. 13b). Similarly, FIG. 14b preserves the locally
high-amplitude reflections present in the raw data section (FIG.
14a).
[0082] FIG. 15a contains raw seismic data from another survey. FIG.
15b shows the result of the voxel suppression technique for this
second data volume. Significant reflections are preserved in each
real data example, especially those associated with major
lithologic contrasts (e.g., salt bodies) contained within the data
volumes.
[0083] Thus, one exemplary operational embodiment of the operation
of the voxel suppression module 160 is a running window operator
that is convolved with the whole volume. For each operator
position, a series of exemplary processing steps are performed.
They are:
[0084] sort the voxels based on absolute value,
[0085] rescale all voxel values to make the local operator's
standard deviation match the global standard deviation,
[0086] preserve the upper user-specified percent of rescaled values
(zero out all other values),
[0087] scale the preserved values based on position within the
operator using a cosine taper, and
[0088] output the tapered values in their original position.
[0089] In this manner, the locally significant amplitude events are
preserved and given a regular expression, while insignificant
reflections are removed. The resulting saved volume is sparse,
including only the rescaled highest amplitude reflectors.
[0090] The voxel suppression module 160 renders a volume sparse by
removing all but the most significant reflections throughout the
volume. The resulting volume emphasizes major acoustic impedance
boundaries. These high impedance contrasts will be present at major
lithology changes. As such, the application of voxel suppression
can be a useful first step to highlight complex interfaces such as
salt boundaries.
[0091] Attributes calculated from 3D seismic data volumes are
commonly noisy and chaotic in their representation of geologic
trends. The complex morphology and expression of geologic features
can result in inconsistent performance of a given attribute for
highlighting features of interest. Structural and diagenetic
overprinting can also complicate attribute results.
[0092] The handling of noise and regularization of uneven attribute
performance is potentially a very important research goal. An
exemplary embodiment of a voxel density technique is a way to score
the local significance of data trends within a 3-D seismic volume.
Significant regions can then be enhanced or normalized, while
insignificant regions can be suppressed or filtered out.
[0093] An exemplary operational embodiment of the voxel density
module 170 includes a running window algorithm carried out by the
running window module. For each operator position, the number of
data points within the window that fall within a given threshold
range are counted; yielding a density score. Areas of high density
score are considered to have high confidence. Conversely, areas of
low density score are assumed to be noise and are filtered out or
deemphasized. Noise can be filtered by removing data points from
regions of low density score. By smoothing high confidence regions
less aggressively, significant edges can be preserved during
smoothing. Volume contrast can also be enhanced in an attribute
volume; boosting the signal-to-noise (S/N) ratio.
[0094] A variety of techniques can be used to control noise in a
data volume. Mean and median filtering are methods of filtering
that work well for random noise. Similarly, wavelet transforms are
another powerful tool for the filtering of random noise. However,
noise is not the only issue that plagues attribute results. Uneven
performance is perhaps a greater impediment to the rapid
utilization attribute results.
[0095] The realities of geology rarely mirror the simplicity of
conceptual models. Factors not accounted for by conceptual models
commonly confuse an attribute designed to image a given geologic
feature. Further complicating attribute performance is the variety
of scales imaged by seismic surveys. Sub-seismic resolution
features can introduce tuning effects into the data that are
indistinguishable from noise by many attributes. FIG. 21b shows the
curvature response of a channel calculated from a flattened data
volume. Although visually useful, the lack of a uniform expression
of the channel complicates manual interpretation and prevents any
automatic handling of the channel interpretation.
[0096] The exemplary voxel density technique uses local data
redundancy to create a stable estimate of confidence in a data set.
Features of interest in a data volume generally persist for some
distance in each direction. The persistence of these features can
be used to overcome their uneven expression in a given data volume.
This is accomplished by the convolution of a 3-D operator with the
data set. A measure of confidence is calculated by the voxel
density module 170 for all voxels. This confidence score can then
be used to guide filtering and enhancement operations.
[0097] The exemplary voxel density module 170 convolves a 3-D
operator with the input data volume. For each position of the
running window operator, the number of voxels that fall within a
given threshold range are counted. The result of this counting
operation is the density score of the window's center voxel. High
density scores indicate voxels of high confidence. Low density
scores highlight voxels of low confidence. In this manner a stable,
non-chaotic, estimate of volume (or attribute) quality can be
achieved. The user can select a specific range of density values
that are significant, and highlight the areas where the values
exist in a high concentration.
[0098] FIG. 16 demonstrates the numerical results of voxel density
calculations on a 10.times.10 data array. FIG. 16a shows a raw data
array containing values between 0 and 9. There exists a diagonal
trend of high values indicated by the gray band in the array. FIG.
16b contains the density scores resulting from calculations on a
3.times.3 operator using accepting all values greater than 6. Edge
effects are handled by scaling the density score by the ratio of
the maximum number of samples possible divided by the local number
of samples in the operator. Thus, in a corner the raw density is
corrected by multiplying it by the ratio of 9 (the total operator
size) divided by 4 (the number of samples in the operator at that
position). FIG. 16c then contains the results of limiting the
output density scores to values of 4 or greater. This thresholding
introduces the ability to filter out regions of low confidence
[0099] Density estimates may be determined in two exemplary ways.
In the first, the voxel density module 170 determines a density
score for every operator position. This is the manner of
calculation used in FIG. 16. In the second, the center voxel must
fall within the specified threshold range for the density score to
be calculated. This additional constraint results in a less smooth
denisty score volume, but has the advantage of not blurring trends
present in the data. The use of this center-pass method of density
scoring makes the voxel density process edge-preserving. A null
value is output for voxels that do not satisfy the center-pass
pre-condition. FIG. 17a contains the density score results of the
10.times.10 sample array requiring that the center voxel fall in
the proper threshold range before continuing with density
calculations. FIG. 17b contains the results of applying a minimum
threshold of 4 to the density scores in FIG. 17a. The `clutter`
present in FIG. 16c has been removed, and the representation of the
high-value trend (highlighted by the gray band) is more
focused.
[0100] FIG. 18 contains a graphical representation of the results
described above. FIG. 18a contains the same raw data as FIG. 16a.
FIG. 18b is the result of outputting the original data array value
for each non-null position in the minimum density thresholded array
(FIG. 16c). FIG. 18c adds the element of requiring the center voxel
to fall in the valid threshold range (similar to FIG. 17b). FIG.
18d shows the result of a simple thresholding operation where no
density calculation was performed. This can be thought of as a
voxel density result from a 1.times.1 operator accepting all values
6 or greater. Clearly, these results are inferior to those present
in FIG. 18c. This difference demonstrates the synergy of combining
thresholding with a 3-D estimate of confidence. In this sense,
voxel density can be thought of as a spatially-weighted
thresholding operation.
[0101] When applied to a data volume, voxel density produces a
density score volume. This volume is similar to the results
presented in FIGS. 16 and 17. The density score volume is a way to
visualize the importance of different trends present in the data.
Areas with coherent, persistent data trends are enhanced, while
incoherent regions are deemphasized.
[0102] The density score volume can also be thought of as a
volumetric confidence estimate. With this estimate of data
confidence, a variety of operations can be performed. These
operations include one or more of noise filtering, edge-preserving
smoothing, and volume contrast enhancement.
[0103] An input data volume can be modified and enhanced in a
variety of ways using the voxel density module 170. The density
score volume can be thought of as an estimate of confidence for
trends in the volume. Using this confidence estimate, it is
possible to enhance the volume through density score-guided
resealing of voxel values. Threshold filtering can remove data that
are not of interest. It is also possible to control the degree of
smoothing, where regions of low confidence are smoothed more than
regions of high confidence.
[0104] Filtering by Density Threshold
[0105] Binary filtering can be accomplished by removing voxels that
have a density score lower than a specified cutoff. In this manner,
insignificant data regions can be removed. Voxels that have a
density score lower than the specified minimum are replaced by null
values. This is demonstrated on a numerical array in FIGS. 18b and
18c. FIG. 20b shows the results of this process being applied to a
Coherence image of a submarine canyon. Similarly, FIG. 21c contains
the result of applying this same procedure to the curvature image
of a flattened channel (FIG. 21b).
[0106] Density-Guided Smoothing
[0107] The same criterion of whether a voxel passes the minimum
density threshold can be used to control smoothing operations
within a data volume. By smoothing voxels that fail the minimum
density test more than voxels that pass the test, insignificant
data regions can be deemphasized. It is also possible to control
which voxels are included in the smoothing operations.
[0108] FIGS. 19a and 19b contain the results of applying a
3.times.3 mean and median filter, respectively, to the raw data in
FIG. 18a. FIG. 19c shows the result of applying the mean smoothing
only to voxels that fail the minimum density test. Voxels that pass
the test are deemed to be high confidence, and are not smoothed.
Similarly, FIG. 19d contains the result of applying a median
operator only to voxels that fail the minimum density test. FIGS.
19e and 19f show the result of modifying the selective smoothing to
only include voxels that fall outside the initial threshold range.
In both cases, this has the result of suppressing the values in the
smoothed regions (darkening them). This results in a greater visual
contrast, and an improved S/N ratio for the sample array. Clearly,
FIGS. 19e & 19f contain superior results for the smoothing of
the raw data array in FIG. 18a.
[0109] FIG. 20c contains the results of applying this
confidence-adaptive smoothing to the Coherence image of a submarine
canyon. Only voxels that failed the minimum density test were
smoothed. Only voxels that fall outside the valid threshold range
were included in smoothing. It should be noted that this method of
adaptive smoothing has preserved the fine detail present in the
edges in the canyon.
[0110] Contrast Enhancement of Data Volumes
[0111] Whether a voxel passes or fails the minimum density test can
be used to control resealing of the data volume. Voxels that pass
are resealed by a factor greater than 1. Those that fail the test
are resealed by a factor less than 1. The precise resealing factor
depends on the original data value, and the density score of that
voxel. In concept, each voxel is resealed by a percent of the
difference between its own value and the extreme value it is being
resealed towards. The percent resealing is controlled by two
equations. For voxels that pass the minimum density test:
Ratiopass=1+(Dscore-Dneutral)/(Nvalues-Dneutral)
[0112] For voxels that fail the minimum density test, the resealing
ratio equation is:
Ratiofail=(Dneutral-Dscore)/Dneutral
[0113] This ratio is then multiplied by the original voxel value to
obtain the resealed voxel value. The addition of a `resealing
strength` term allows for a more subtle resealing operation. FIG.
22 contains the numerical and graphical results of applying this
type of resealing to the sample data array. The trend of high
values in FIG. 16a (highlighted by the gray band) has been
enhanced, while suppressing the scatter of values that surround it.
FIG. 22b has a much improved S/N ratio over the original array in
FIG. 18a. The effect on the data histogram is also shown.
[0114] FIG. 20d shows the result of applying this operation to the
Coherence image of the submarine canyon. This resealing was done
with a strength of 0.5. The same type of contrast enhancement was
performed on the curvature image of a channel for FIG. 21d. As with
the adaptive smoothing, significant edges have been preserved in
the data while improving the S/N ratio.
[0115] Locally Adaptive Operations
[0116] It is also possible to modify the resealing operation to
only rescale locally high values. By linking the threshold range to
local variance in the data volume, only locally high voxels will be
counted in the density calculations. This prevents a high noise
background from overwhelming the voxel density process, and
provides a more robust result where the characteristic voxel value
of a feature varies significantly. FIG. 23b is a Coherence image
showing a series of faults. It can be visually determined that a
single threshold range cannot represent all sections of the faults
present without allowing much of the surrounding noise to also be
enhanced. The significant data are recognized more by their linear
trends and being locally high values. By linking the threshold
range to local variance, these faults can be enhanced while
deemphasizing the surrounding noise.
[0117] FIG. 23 shows the effect of locally adaptive voxel
density-controlled smoothing and contrast enhancement on a time
slice from a Gulf of Mexico data set. FIG. 23a contains the raw
data that have had a light (3.times.3) median filter applied to
reduce random noise. FIG. 23b is the result of Coherence to the
data volume. FIGS. 23d & 23e were produced using locally
variable thresholds controlled by the variance distribution seen in
FIG. 23c. Areas with higher variance (tending towards a lighter
gray) cause a tighter threshold range to be used in the density
calculations. FIGS. 23d & 23e contain locally adaptive contrast
enhancement and smoothing of the raw Coherence slice in FIG. 23b,
respectively.
[0118] FIG. 24 demonstrates the same operations applied to a deeper
time slice of the same volume. A single threshold range would not
successfully capture all the significant data trends present in
FIG. 24b. Thus, a locally adaptive variance-controlled approach to
voxel density calculations yields good results. FIGS. 24c & 24d
contain contrast enhanced and smoothed results, respectively.
[0119] FIG. 25a is a Coherence image showing a portion of a salt
body, with surrounding faults. FIGS. 25b & 25c show the
contrast enhanced data. The overall trends of the faults are
preserved, while the surrounding insignificant data regions are
reduced in value. These data volumes were then processed with fault
enhance, an attribute that enhances dipping planar features in a
Coherence volume. FIG. 25d is the fault enhanced output for the raw
Coherence image. It is hindered by a multitude of spurious
lineaments. These erroneous data are greatly reduced when the fault
enhanced volume are processed from the contrast enhanced coherence
volumes. FIG. 25f has the highest S/N ratio of the three fault
enhance volumes. It was calculated from the highest contrast volume
(FIG. 25c).
[0120] The voxel density module provides a way to score confidence
in a data volume. The number of voxels that fall within a given
threshold range are counted within a moving 3-D operator. This
result of this count is the density score of the operator's center
voxel. Voxels with a high density score are considered significant,
while those with a low density score can be considered noise.
Significant data regions can be preserved or enhanced, while
insignificant data regions are smoothed or filtered out.
[0121] Voxel density-guided smoothing and resealing operations are
edge preserving. Significant trends can be enhanced while
maintaining their overall shape and internal texture. This is
accomplished by selectively smoothing insignificant areas more than
valid data trends. Similarly, significant data trends can be
selectively gained upward while muting surrounding noise. Such an
operation preserves the original flavor of the data, but with an
increased S/N ratio.
[0122] The threshold range included in density scoring can be
linked to local variance in the data volume. In this manner,
locally significant data trends are preserved. This allows voxel
density to be used for data sets that have significant data value
ranges that vary between data regions.
[0123] Voxel density represents a potentially very valuable tool
when used to pre-process data for visual and automated
interpretation. For example, at least a S/N ratio can be improved,
and visual emphasis can be given to important trends through
selective smoothing.
[0124] FIG. 2 illustrates an exemplary embodiment of determining
voxel connectivity in accordance with this invention. In
particular, control begins in step S200 and continues to step S210.
In step S210, a data volume, such as a seismic data volume is
input. Next, in step S220, connected non-null voxels are mapped.
Then, in step S230, the connectivity score is determined in
accordance with the number of constituent voxels. Control then
continues to step S240.
[0125] In optional step S240, and based on a selectivity score,
features can be filtered. Similarly, in step S250, features can be
filtered if they are within a connectivity range. Control then
continues to step S260.
[0126] In step S260, a visual-clutter reduced seismic data volume
is output and saved. Control then continues to step S270 where the
control sequence ends.
[0127] FIG. 3 illustrates an exemplary method of reducing
reflections according to this invention. Specifically, control
begins in step S300 and continues to step S310. In step S310, a
seismic data volume is input. For example, the input data volume
can be the data volume saved in the process illustrated in FIG. 2.
Next, in step S320, the amplitude of all reflection lobes can
optionally be regularized with control continuing to step S330.
[0128] In step S330, and for each operator position, steps
S332-S338 are performed. In particular, in step S332, a
determination is made whether the operator's center voxel has the
highest absolute amplitude of all voxels within the operator. Next,
in step S334, and if the highest amplitude voxel is not in the
center of the operator, the process moves to the next voxel.
[0129] In step S336, and if the highest absolute amplitude voxel is
at the center of the operator, the voxel value is written to the
output volume in its original position. Next, in step S337, a
search upward and downward from the center voxel is performed to
determine the extent of the main reflection lobe. Then, in step
S338, the full extent of the main reflection lobe is saved to the
output. Control then continues to step S340 where the control
sequence ends.
[0130] FIG. 4 illustrates an exemplary method of voxel suppression
with control beginning in step S400 with the input of a volume,
such as the volume saved in FIG. 3 and continuing to Step S410. In
step S410, and for each operator position, all voxels are sorted by
absolute value. Next, in step S420, the voxels above a
user-specified value are preserved in their original position.
Then, in step S430, rescaling to regularize is optionally performed
in accordance with RF=Whole volume Std.Dev/Operator Std.Dev.
Control then continues to step S440.
[0131] In step S440, voxels in the center of the operator are
rescaled such that they are emphasized. Next, in step S450, the
visually improved volume is output and saved.
[0132] FIG. 5 illustrates an exemplary voxel density determination
method. Control begins in step S500 with the input of volume, such
as the volume saved in FIG. 4, and continues to step S510. In step
S510, and for each position of a running window, the number of
voxels within a given threshold range are counted. Next, in step
S520, the density score of the window's center voxel is output.
This is accomplished by subroutines S522 and S524. Specifically, in
step S522, the density sore for every operator position is
determined. Then, in step S524, if the center voxel falls within a
specified threshold range, the density score is calculated, with a
higher density score being correlated to voxels of high confidence
and a lower density score being correlated to voxels of lower
confidence.
[0133] In step S530, the density score volume is output and saved,
for example, as a volumetric confidence estimate. Control then
continues to step S540 where the control sequence ends.
[0134] FIGS. 26-30 illustrate various exemplary techniques
disclosed herein applied to seismic data. It should be appreciated
however that the techniques disclosed herein can also be used on
other types of data such as medical imaging data, 2- or 3-D data
representing an article, body, object(s) or the like.
[0135] FIG. 26 illustrates voxel density applied to coherence.
Panel (a) contains a coherence image of a submarine canyon. Panel
(b) shows the result of applying binary voxel density filtering to
the data in Panel (a). Voxels that fail the minimum density
threshold test are assigned a null value. Panel (c) show the result
of voxel density controlled smoothing. Voxel density scores are
used to alter the data contrast in Panel (d). Voxel
density-controlled smoothing and contrast enhancement preserve the
original context of the data, rather than simply removing voxels
that fail the density threshold test.
[0136] FIG. 27 illustrates an example of voxel suppression. Panel
(a) contains a raw amplitude section. The top and bottom of the
horizontal tabular salt body are indicated by the arrows in panel
(a). Panel (b) shows the result of applying voxel suppression to
the data in panel (a). Higher amplitude events (including those
associated with the salt body) have been preserved, while most of
the surrounding low amplitudes have been removed. Some scattered
reflection fragments remain (as indicated by the arrow in panel
(b).
[0137] FIG. 28 illustrates an example of the result of reflection
collapsing. Specifically, the reflection collapsing technique was
applied to sparse seismic data. Panel (a) contains a voxel
suppression result. The data are now sparse. This sparseness
improves the performance of the reflection collapser (panel (b)).
Panels (c) and (d) were processed with the added constraint that
only peaks or troughs would be considered in this exemplary
reflection collapser operation, respectively. Panel (c) contains
the best result for highlighting the tabular salt body indicated by
the arrows in panel (a).
[0138] FIG. 29 illustrates an example of the voxel connectivity
technique. Panel (a) contains a voxel suppression result. The top
and bottom of the horizontal tabular salt body are indicated by the
arrows in panel (a). Panel (b) shows the result of removing all
connected voxel bodies that consist of fewer than 200 voxels. Panel
(c) contains the better result of filtering out all connected
bodies with fewer than 800 member voxels.
[0139] FIG. 30 illustrates an exemplary attribute sequence. In this
attribute sequence the whole workflow described herein is applied
to real data. Panel (a) contains an amplitude slice from a North
Sea data volume. The location of the top of salt is indicated by
the arrow in panel (a). Panel (b) shows the result of applying
Voxel Suppression the data shown in panel (a). Note that the
majority of non-salt reflections have been filtered out (as
highlighted by the arrows). The result of applying reflection
collapser to the voxel suppression output is shown in panel (c).
Panel (d) contains the final result filtered by voxel density. The
scattered reflection fragments highlighted by the arrows in panel
(c) have been removed.
[0140] While the above-described flowcharts have been discussed in
relation to a particular sequence of events, it should be
appreciated that changes to this sequence can occur without
materially effecting the operation of the invention. Additionally,
the exact sequence of events need not occur as set forth in the
exemplary embodiments. Additionally, the exemplary techniques
illustrated herein are not limited to the specifically illustrated
embodiments but can also be utilized with the other exemplary
embodiments and each described feature is individually and
separately claimable.
[0141] The systems, methods and techniques of this invention can be
implemented on a special purpose computer, a programmed
microprocessor or microcontroller and peripheral integrated circuit
element(s), an ASIC or other integrated circuit, a digital signal
processor, a hard-wired electronic or logic circuit such as
discrete element circuit, a programmable logic device such as PLD,
PLA, FPGA, PAL, any means, or the like. In general, any device
capable of implementing a state machine that is in turn capable of
implementing the methodology illustrated herein can be used to
implement the various methods and techniques according to this
invention.
[0142] Furthermore, the disclosed methods may be readily
implemented in processor executable software using object or
object-oriented software development environments that provide
portable source code that can be used on a variety of computer or
workstation platforms. Alternatively, the disclosed system may be
implemented partially or fully in hardware using standard logic
circuits or VLSI design. Whether software or hardware is used to
implement the systems in accordance with this invention is
dependent on the speed and/or efficiency requirements of the
system, the particular function, and the particular software or
hardware systems or microprocessor or microcomputer systems being
utilized. The systems, methods and techniques illustrated herein
can be readily implemented in hardware and/or software using any
known or later developed systems or structures, devices and/or
software by those of ordinary skill in the applicable art from the
functional description provided herein and with a general basic
knowledge of the computer and geologic arts.
[0143] Moreover, the disclosed methods may be readily implemented
in software that can be stored on a computer-readable storage
medium, executed on programmed general-purpose computer with the
cooperation of a controller and memory, a special purpose computer,
a microprocessor, or the like. The systems and methods of this
invention can be implemented as program embedded on personal
computer such as an applet, JAVA.RTM. or CGI script, in C or C++,
Fortran, or the like, as a resource residing on a server or
computer workstation, as a routine embedded in a dedicated system
or system component, or the like. The system can also be
implemented by physically incorporating the system and/or method
into a software and/or hardware system, such as the hardware and
software systems of a dedicated seismic interpretation device.
[0144] It is therefore apparent that there has been provided, in
accordance with the present invention, systems and methods for
interpreting data. While this invention has been described in
conjunction with a number of embodiments, it is evident that many
alternatives, modifications and variations would be or are apparent
to those of ordinary skill in the applicable arts. Accordingly, it
is intended to embrace all such alternatives, modifications,
equivalents and variations that are within the spirit and scope of
this invention.
* * * * *